For the second strategy (w/o detection and w/ correction), we just select clean labels according to the activation scores from initial CAM. Similar to [1]}, we consider pixels with activation scores larger than 0.3 and smaller than 0.05 as foreground and background clean labels, and use them as the supervision for training GAT. Such strategy is similar with [2]}, but using GAT instead of GCN. However, the segmentation result is only 59.6%, which is much lower than that of using our proposed detection strategy. The result illustrates that the seed
regions with high confidence contain many mislabeled pixels and [2]} is not suitable for image-level supervisions. For the third strategy (w/ detection and w/ correction), our approach achieves the best segmentation result of 67.5%. This clearly validates the effectiveness of the proposed detection and correction method.
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